21 citations · 50 across the 17 of their papers we have counts for
20 papers
Beyond Homology: Non-Abelian and Orientability Invariants Sensitivity in Cellular Networks
Raghuwansh Raj, George Panagopoulos, Jun Pang
Topological deep learning (TDL) architectures are typically evaluated on synthetic complex pairs that differ in a target invariant. Yet, whether a model recovers that invariant or…
Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation
George Panagopoulos
Estimating heterogeneous treatment effects with machine learning has attracted substantial attention in both academic research and industrial practice. However, the two communities…
Efficient Data Selection for Training Genomic Perturbation Models
George Panagopoulos, Johannes F. Lutzeyer, Sofiane Ennadir +2
Genomic studies face a vast hypothesis space, while interventions such as gene perturbations remain costly and time-consuming. To accelerate such experiments, gene perturbation mod…
Integrating Optimal Transport and Structural Inference Models for GRN Inference from Single-cell Data
Tsz Pan Tong, Aoran Wang, George Panagopoulos +1
We introduce a novel gene regulatory network (GRN) inference method that integrates optimal transport (OT) with a deep-learning structural inference model. Advances in next-generat…
Metrics to Detect Small-Scale and Large-Scale Citation Orchestration
Iakovos Evdaimon, John P. A. Ioannidis, Giannis Nikolentzos +3
Citation counts and related metrics have pervasive uses and misuses in academia and research appraisal, serving as scholarly influence and recognition measures. Hence, comprehendin…
KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning
Roman Bresson, Giannis Nikolentzos, George Panagopoulos +3
In recent years, Graph Neural Networks (GNNs) have become the de facto tool for learning node and graph representations. Most GNNs typically consist of a sequence of neighborhood a…
Uplift Modeling Under Limited Supervision
George Panagopoulos, Daniele Malitesta, Fragkiskos D. Malliaros +1
Estimating causal effects in e-commerce tends to involve costly treatment assignments which can be impractical in large-scale settings. Leveraging machine learning to predict such…
Exploratory Analysis of Academic Collaborations between French and US
George Panagopoulos, Michalis Vazirgiannis
International academic collaborations cultivate diversity in the research landscape and facilitate multiperspective methods, as the scope of each country's science depends on its n…
Gauge-invariant renormalization of fermion bilinears and energy-momentum tensor on the lattice
G. Spanoudes, M. Costa, I. Karpasitis +4
We study a gauge-invariant renormalization scheme (GIRS) for composite operators, regularized on the lattice, by extending the coordinate space (X-space) scheme proposed some years…
Perturbative study of the Gluino-Glue operator in SYM
Marios Costa, George Panagopoulos, Haralambos Panagopoulos +3
We investigate the renormalization of the Gluino-Glue operator, using both Lattice Perturbation Theory (LPT) and a Gauge Invariant Renormalization Scheme (GIRS). The latter scheme…
Maximizing Influence with Graph Neural Networks
George Panagopoulos, Nikolaos Tziortziotis, Michalis Vazirgiannis +1
Finding the seed set that maximizes the influence spread over a network is a well-known NP-hard problem. Though a greedy algorithm can provide near-optimal solutions, the subproble…
Can Author Collaboration Reveal Impact? The Case of h-index
Giannis Nikolentzos, George Panagopoulos, Iakovos Evdaimon +1
Scientific impact has been the center of extended debate regarding its accuracy and reliability. From hiring committees in academic institutions to governmental agencies that distr…
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
Benedek Rozemberczki, Paul Scherer, Yixuan He +8
We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing. The main goal of…
Gauge-invariant Renormalization of the Gluino-Glue operator
Marios Costa, George Panagopoulos, Haralambos Panagopoulos +1
We study the Gluino-Glue operator in the context of Supersymmetric Yang-Mills (SYM) theory. This composite operator is gauge invariant, and it is directly connected…
Two-loop renormalization and mixing of gluon and quark energy-momentum tensor operators
George Panagopoulos, Haralambos Panagopoulos, Gregoris Spanoudes
In this paper, we present one- and two-loop results for the renormalization of the gluon and quark gauge-invariant operators which appear in the definition of the QCD energy-moment…
A new branch of inflationary speed limits
Dayshon Mathis, Alexandros Mousatov, George Panagopoulos +1
We present a new mechanism for inflation which exhibits a speed limit on scalar motion, generating accelerated expansion even on a steep potential. This arises from explicitly inte…
Transfer Graph Neural Networks for Pandemic Forecasting
George Panagopoulos, Giannis Nikolentzos, Michalis Vazirgiannis
The recent outbreak of COVID-19 has affected millions of individuals around the world and has posed a significant challenge to global healthcare. From the early days of the pandemi…
Performance in the Courtroom: Automated Processing and Visualization of Appeal Court Decisions in France
Paul Boniol, George Panagopoulos, Christos Xypolopoulos +3
Artificial Intelligence techniques are already popular and important in the legal domain. We extract legal indicators from judicial judgment to decrease the asymmetry of informatio…
Multi-task Learning for Influence Estimation and Maximization
George Panagopoulos, Fragkiskos D. Malliaros, Michalis Vazirgiannis
We address the problem of influence maximization when the social network is accompanied by diffusion cascades. In prior works, such information is used to compute influence probabi…
A Review of Network Inference Techniques for Neural Activation Time Series
George Panagopoulos
Studying neural connectivity is considered one of the most promising and challenging areas of modern neuroscience. The underpinnings of cognition are hidden in the way neurons inte…